Godly Kolo completed Energy and Renewable: Machine Learning for the Power Grid

Built a leakage-aware gas-turbine TEY predictor end to end: selected the eight legitimate sensor features, excluded downstream CO/NOx emissions, and validated on a locked later-period test split. Compared a LinearRegression baseline with a RandomForest model and showed the forest improved held-out MAE from 2.90 to 1.53 MWh with R² rising to 0.983. Also authored a concise model card covering intended use, limitations, and drift monitoring.

• Leakage-Free Feature Selection
• Time-Based Model Validation
• Honest Held-Out Evaluation
• Model Risk Communication